SemiAnalysis Weekly

Ep. 012 - Cerebras IPO: 1,100 Tokens Per Second vs GPU's 500 (AI Cloud TCO) | Jordan Nanos, Howie , Myron Xie

Jordan Nanos (@JordanNanos), Howie, and Myron Xie break down the economics of Cerebras's IPO on the eve of their public debut, examining their OpenAI and Amazon deals that have shifted the company away from Middle Eastern investor concentration toward frontier AI labs willing to pay exponentially more for speed.The discussion covers Cerebras's radical stitching innovation across a full wafer, creating compute density equivalent to an entire NVL72 rack without off-chip data movement. The hosts analyze whether businesses will accept these premium economics as fast tokens become the new standard for interactive AI applications.Subscribe for weekly deep dives into semiconductor economics and AI infrastructure developments. (00:00) Cerebras IPO Preview (18:58) Need for Speed Fast Tokens (21:19) Wafer Scale Engine Architecture (25:31) Radical Stitching Innovation (31:55) Power Delivery and Cooling (34:31) Bandwidth and IO Limitations (37:12) Scaling Beyond Wafer Size (40:05) Manufacturing and Assembly Bottlenecks (42:54) Data Center Service Model

Ep. 011 - GPT 5.5 vs Claude 4.7: OpenAI's Comeback From the Brink (Tokenomics) | Jordan Nanos, Dylan Patel, Doug O'Laughlin, Max Kan

OpenAI was in serious trouble at the beginning of this year. Anthropic's Claude Opus 4.5 release had triggered a wave of developers to start using Claude Code, pushing Anthropic's revenue past OpenAI's on a like-for-like basis by April. OpenAI's GPT 5.4 response was such an embarrassment they didn't even compare it to Claude in their model release card. Then came GPT 5.5 - finally back on the frontier, but is it enough to reclaim the crown? Jordan Nanos (@JordanNanos), Dylan Patel (@Dylan522p), Doug O'Laughlin (@FabricatedKnowledge), and Max Kan (@maxkan_) break down the latest AI model wars, from Claude 4.7's coding dominance to DeepSeek's long-delayed v4 release and what it reveals about China's AI capabilities. They analyze token efficiency, benchmark gaming, and why fast mode might be fake news. Subscribe for weekly deep dives into the semiconductor and AI infrastructure powering the future. The Coding Assistant Breakdown [https://newsletter.semianalysis.com/p/the-coding-assistant-breakdown-more?_gl=1*1kdxhrs*_ga*MTY1NDExMjk2Ny4xNzc2MTIzOTQ1*_ga_FKWNM9FBZ3*czE3NzgwMjUzODEkbzI2JGcwJHQxNzc4MDI1MzgxJGo2MCRsMCRoMjEyNzMwMzMyNw..] AI Value Capture [https://newsletter.semianalysis.com/p/ai-value-capture-the-shift-to-model?_gl=1*1x68y7d*_ga*MTY1NDExMjk2Ny4xNzc2MTIzOTQ1*_ga_FKWNM9FBZ3*czE3NzgwMjUzODEkbzI2JGcwJHQxNzc4MDI1MzgxJGo2MCRsMCRoMjEyNzMwMzMyNw..] Timestamps: 00:00 OpenAI's Comeback and the Latest AI Model Wars 04:05 The High Cost of AI Models and Fast Mode Effectiveness 08:16 When AI Tokens Become Too Expensive for Tasks 13:11 Why AI Model Quality Degrades and Benchmarks Fail 18:42 Deep Dive into Claude 4.7 Features and Tokenizer Changes 25:29 DeepSeek's Release and China's AI Compute Constraints 28:20 The Future of Context Windows and Agent Orchestration 30:47 The Great Debate: CLI vs. App for AI Interaction 36:33 Debunking AI Fake News and Context Window Limitations 40:51 The AI Race: China, Meta, and the Neo Cloud Vision 43:46 Final Thoughts and Listener Feedback Request

Ep. 010 - How Much Do GPUs Really Cost, and Where Does the Value Go? (AI Cloud TCO) | Jordan Nanos, Dan Nishball, Kang Wen Cheang, Zane Fong

This episode features Jordan Nanos (@JordanNanos) and Daniel Nishball (@dnishball) breaking down the economics of GPU clusters through real-world data and experience. Joined with Kang Wen Cheang and Zane Fong, the team discussed moving beyond theoretical TCO models as they examine how reliability differences between top-tier and lower-tier providers create significant cost disparities that aren't captured in simple per-GPU pricing. The discussion introduces practical frameworks for measuring goodput and understanding how system failures cascade through entire training jobs.Nanos walks through the mechanics of fault-tolerant frameworks including AWS's Checkpointless Training and explains why a single GPU failure can halt progress across hundreds of nodes. The conversation reveals how hyperscalers and NeoClouds price their services and why paying premium rates for reliable infrastructure often delivers better value than chasing the lowest per-hour costs. Subscribe to SemiAnalysis for in-depth analysis of AI hardware economics and infrastructure trends that impact the entire semiconductor ecosystem.

Ep. 009 - Using Open Source Data To Drive Investment Decisions (ChipBook) | Chaim Eisenberg, Simi Sherman, Jordan Nanos

This week the team from ChipBook (formerly Chips & Wafers) joins. Jordan Nanos talks with Chaim Eisenberg and Simi Sherman as they explore how they build the ChipBook with open source data, and how that drives investment decisions. This episode dives deep into the ChipBook itself, revealing how granular, historical data collection provides insights into supply chain dynamics, memory markets, wafer fabrication equipment trends and more. The guests also share compelling examples of how their data-driven approach has generated some viral social media recently.for more: SemiAnalysis.com/chipbook00:00 The Chipbook: Understanding Open Source Data07:56 Granularity in Data: The Key to Investment Insights09:11 Understanding the Semiconductor Supply Chain10:52 Memory Market Insights and Trends14:23 Tracking WFE and Its Impact on Production15:20 The Importance of Early Signals in Investment16:51 Geopolitical Implications on Semiconductor Supply20:24 The Impact of Tariffs and Regulations22:48 Granular Tracking for Investment Decisions27:07 Data-Driven Insights and Investment Strategies29:13 The Structure of the Chipbook33:02 Collaboration and Integration at Semi Analysis37:02 Geopolitical Analysis and Its Impact on TSMC43:48 Helium Supply Chain and Its Importance

Ep. 008 Claude Code Psychosis: How SemiAnalysis Is Token Mogging Meta | Dan Nishball, Sam Harshe, Jordan Nanos

Jordan Nanos (@jordannanos) Daniel Nishball (@dnishball) and Sam Harshe (@sharshe02) break down how SemiAnalysis is deploying Claude Code agents across its Singapore office at a scale that outpaces Meta on a per-employee basis. They cover the practical workflow changes, the trust and reliability questions that come with AI-generated analysis, and what it actually takes to build an agent swarm that does useful work. The conversation also gets into cybersecurity risks and where AI model development is headed next.00:00 - Introduction and Team Dynamics09:10 - The Evolution of Agent Utilization14:49 - Conference Insights and Research Efficiency15:58 - AI's Role in Learning and Analysis21:13 - Trust and Reliability in AI Outputs27:04 - Market Impact and Adoption of AI Tools32:14 - Cybersecurity and AI: Opportunities and Challenges39:32 - Future of AI Models and User Experience